At HOV, a core value embedded in us is that of ‘Learning Machine.’ Everyone is encouraged to work on something that they believe could be their superpower. I have been working in QA and cross checking of data research, but earlier this year I decided to ‘dabble in the data’ as I enjoy doing data research and am always curious to learn more. The biggest challenge, of course, was how to make the switch and where to begin?
Like most beginners, I began researching Data Analytics and focused on introductory topics. However, the subject focus was fairly general and as a beginner I still had a thousand questions on my mind. With the help of my mentors at HOV, I decided to search instead for specific skills needed while applying for a Data Analyst position; this formed the beginning point of my journey. I also took the initiative to enroll in online courses while managing my full-time role at HOV.
Choosing courses is never easy either, since there are limitless options and you’re starting out so you aren’t too sure which ones would be helpful. After trying out several courses, the one I would highly recommend is “What is Data Science?”
In this course, topics are discussed based on experiences of actual data scientists all from different backgrounds. Want to know their secret skill? Passion! They all had a burning desire to continuously learn more, use new tools and practice to clean and analyze data. That’s when I realized that I could be one of them too… and if you put your heart to it, so can you!
So if you’re still confused about where to start or if you should start, here are some tips from an aspiring and new Data Scientist:
1. Be curious 🕵🏽♀️
Curiosity is an absolute must. You have to ask questions to clarify the business need and come to a solution. Start asking simple but key questions like the following: “What data is required to solve the problem, and where will that data come from?”
2. Argumentative 🏁
Don’t be afraid to start somewhere and then learn from the data itself. It’s always going to be a learning process; modify your assumptions, hypothesis and data! By learning, we’ll have the ability to tell great stories while presenting our data.
3. Judgements 📚
In most contexts it isn’t advised to have preconceived ideas about things but with data analytics, the more you know the easier the decision. Heading into a problem with a fairly good idea about it will always help you decide where to begin.
4. Decide what expertise or industry you’re interested in, and what your competitive advantage is 💪🏾
Once you figure out where your expertise lie, start acquiring the analytical skills; what platforms you should learn and which of those platforms or tools would be specific to the industry that interests you. Once you have got some proficiency in the tools, the next thing would be to apply your skills to real problems!
Pro-tip: Try using Task Relevant Maturity (TRM) boards as roadmaps. Here’s mine ⬇️
I hope you find these tips helpful in your data analytics journey!